code: def setUpClass(cls):
      cls.config = Config(
          dict(
              in_channels=[
                  256,
                  512,
                  1024,
                  2048,
              ],  # pass to pixel_decoder inside
              strides=[4, 8, 16, 32],
              feat_channels=256,
              out_channels=256,
              num_classes=40,
              num_frames=2,
              num_queries=100,
              num_transformer_feat_level=3,
              pixel_decoder=dict(
                  type="MSDeformAttnPixelDecoder",
                  num_outs=3,
                  norm_cfg=dict(type="GN", num_groups=32),
                  act_cfg=dict(type="ReLU"),
                  encoder=dict(
                      num_layers=6,
                      layer_cfg=dict(
                          self_attn_cfg=dict(
                              embed_dims=256,
                              num_heads=8,
                              num_levels=3,
                              num_points=4,
                              im2col_step=128,
                              dropout=0.0,
                              batch_first=True,
                          ),
                          ffn_cfg=dict(
                              embed_dims=256,
                              feedforward_channels=1024,
                              num_fcs=2,
                              ffn_drop=0.0,
                              act_cfg=dict(type="ReLU", inplace=True),
                          ),
                      ),
                  ),
                  positional_encoding=dict(
                      num_feats=128, normalize=True
                  ),
              ),
              enforce_decoder_input_project=False,
              positional_encoding=dict(
                  type="SinePositionalEncoding3D",
                  num_feats=128,
                  normalize=True,
              ),
              transformer_decoder=dict(  # Mask2FormerTransformerDecoder
                  return_intermediate=True,
                  num_layers=9,
                  layer_cfg=dict(  # Mask2FormerTransformerDecoderLayer
                      self_attn_cfg=dict(  # MultiheadAttention
                          embed_dims=256,
                          num_heads=8,
                          dropout=0.0,
                          batch_first=True,
                      ),
                      cross_attn_cfg=dict(  # MultiheadAttention
                          embed_dims=256,
                          num_heads=8,
                          dropout=0.0,
                          batch_first=True,
                      ),
                      ffn_cfg=dict(
                          embed_dims=256,
                          feedforward_channels=2048,
                          num_fcs=2,
                          ffn_drop=0.0,
                          act_cfg=dict(type="ReLU", inplace=True),
                      ),
                  ),
                  init_cfg=None,
              ),
              loss_cls=dict(
                  type="CrossEntropyLoss",
                  use_sigmoid=False,
                  loss_weight=2.0,
                  reduction="mean",
                  class_weight=[1.0] * 40 + [0.1],
              ),
              loss_mask=dict(
                  type="CrossEntropyLoss",
                  use_sigmoid=True,
                  reduction="mean",
                  loss_weight=5.0,
              ),
              loss_dice=dict(
                  type="DiceLoss",
                  use_sigmoid=True,
                  activate=True,
                  reduction="mean",
                  naive_dice=True,
                  eps=1.0,
                  loss_weight=5.0,
              ),
              train_cfg=dict(
                  num_points=12544,
                  oversample_ratio=3.0,
                  importance_sample_ratio=0.75,
                  assigner=dict(
                      type="mmdet.HungarianAssigner",
                      match_costs=[
                          dict(
                              type="mmdet.ClassificationCost",
                              weight=2.0,
                          ),
                          dict(
                              type="mmdet.CrossEntropyLossCost",
                              weight=5.0,
                              use_sigmoid=True,
                          ),
                          dict(
                              type="mmdet.DiceCost",
                              weight=5.0,
                              pred_act=True,
                              eps=1.0,
                          ),
                      ],
                  ),
                  sampler=dict(type="mmdet.MaskPseudoSampler"),
              ),
          )
      )
  
file_path: mmdetection/tests/test_models/test_tracking_heads/test_mask2former_track_head.py
imports: from unittest import TestCase
  import torch
  from mmengine import Config
  from mmdet.models.tracking_heads import Mask2FormerTrackHead
  from mmdet.structures import DetDataSample, TrackDataSample
  from mmdet.testing import demo_track_inputs
label_desc: Use image node_id 1 for calling the TestMask2FormerHead obj's underlying member method code with example usage: obj.setUpClass(cls) without return types